""" Optimize the Sellar problem using SLSQP."""
import numpy as np
from openmdao.api import Component, Group, IndepVarComp, ExecComp, NLGaussSeidel, ScipyGMRES
[docs]class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self):
super(SellarDis1, self).__init__()
# Global Design Variable
self.add_param('z', val=np.zeros(2))
# Local Design Variable
self.add_param('x', val=0.)
# Coupling parameter
self.add_param('y2', val=0.)
# Coupling output
self.add_output('y1', val=1.0)
[docs] def solve_nonlinear(self, params, unknowns, resids):
"""Evaluates the equation
y1 = z1**2 + z2 + x1 - 0.2*y2"""
z1 = params['z'][0]
z2 = params['z'][1]
x1 = params['x']
y2 = params['y2']
unknowns['y1'] = z1**2 + z2 + x1 - 0.2*y2
[docs] def linearize(self, params, unknowns, resids):
""" Jacobian for Sellar discipline 1."""
J = {}
J['y1','y2'] = -0.2
J['y1','z'] = np.array([[2*params['z'][0], 1.0]])
J['y1','x'] = 1.0
return J
[docs]class SellarDis2(Component):
"""Component containing Discipline 2."""
def __init__(self):
super(SellarDis2, self).__init__()
# Global Design Variable
self.add_param('z', val=np.zeros(2))
# Coupling parameter
self.add_param('y1', val=0.)
# Coupling output
self.add_output('y2', val=1.0)
[docs] def solve_nonlinear(self, params, unknowns, resids):
"""Evaluates the equation
y2 = y1**(.5) + z1 + z2"""
z1 = params['z'][0]
z2 = params['z'][1]
y1 = params['y1']
# Note: this may cause some issues. However, y1 is constrained to be
# above 3.16, so lets just let it converge, and the optimizer will
# throw it out
y1 = abs(y1)
unknowns['y2'] = y1**.5 + z1 + z2
[docs] def linearize(self, params, unknowns, resids):
""" Jacobian for Sellar discipline 2."""
J = {}
J['y2', 'y1'] = .5*params['y1']**-.5
J['y2', 'z'] = np.array([[1.0, 1.0]])
return J
[docs]class SellarDerivatives(Group):
""" Group containing the Sellar MDA. This version uses the disciplines
with derivatives."""
def __init__(self):
super(SellarDerivatives, self).__init__()
self.add('px', IndepVarComp('x', 1.0), promotes=['x'])
self.add('pz', IndepVarComp('z', np.array([5.0, 2.0])), promotes=['z'])
self.add('d1', SellarDis1(), promotes=['x', 'z', 'y1', 'y2'])
self.add('d2', SellarDis2(), promotes=['z', 'y1', 'y2'])
self.add('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]) ),
promotes=['obj', 'x', 'z', 'y1', 'y2'])
self.add('con_cmp1', ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
self.add('con_cmp2', ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])
self.nl_solver = NLGaussSeidel()
self.nl_solver.options['atol'] = 1.0e-12
self.ln_solver = ScipyGMRES()
if __name__ == '__main__':
from openmdao.api import Problem, ScipyOptimizer, SqliteRecorder
# Setup and run the model.
top = Problem()
top.root = SellarDerivatives()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1.0e-8
# This is for testing with SNOPT via pyOptSparse
'''
from openmdao.api import pyOptSparseDriver
top.driver = pyOptSparseDriver()
top.driver.options['optimizer'] = 'SNOPT'
'''
top.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
top.driver.add_desvar('x', lower=0.0, upper=10.0)
top.driver.add_objective('obj')
top.driver.add_constraint('con1', upper=0.0)
top.driver.add_constraint('con2', upper=0.0)
rec = SqliteRecorder('sellar_snopt.db')
top.driver.add_recorder(rec)
rec.options['record_derivs'] = True
top.setup()
top.run()
print("\n")
print( "Minimum found at (%f, %f, %f)" % (top['z'][0],
top['z'][1],
top['x']))
print("Coupling vars: %f, %f" % (top['y1'], top['y2']))
print("Minimum objective: ", top['obj'])
